An ensemble Kalman filtering algorithm for state estimation of jump Markov systems. (2016)

• Record Type: Journal Article
• Authors: Vasuhi, S.; Vaidehi, V.
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 8:Number 1(2016)
• Page Start: 1
• Page End: 7
• Publication Date: 2016
• Subjects: ensemble Kalman filter -- EnKF -- nonlinear state estimation -- jump Markov systems -- dymanic modelling -- target tracking; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

This paper presents a target model whose dynamics is modelled as a jump Markov process. The problem of state estimation of jump Markov system using ensemble Kalman filter (EnKF) is dealt here. The EnKF is designed for higher order nonlinear state estimation. In target tracking using jump process, accuracy of the state estimate is defined as a function of ensemble size. The jump Markov system minimises state error at the state estimation and assumes that all probability distributions involved are to be Gaussian. This paper proposes state estimation of jump Markov systems using EnKF, at each step the ensemble of state estimates are calculated. From the result, calculate the error statistics from ensemble forecasts which are dynamically propagated through nonlinear system.